Image-Text-to-Text
Transformers
Safetensors
multimodal
benchmark
vision-language
agentic-ai
business-ideation
qwen2-vl
Instructions to use hchoi256/mba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hchoi256/mba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hchoi256/mba")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hchoi256/mba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hchoi256/mba with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hchoi256/mba" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hchoi256/mba", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hchoi256/mba
- SGLang
How to use hchoi256/mba with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "hchoi256/mba" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hchoi256/mba", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "hchoi256/mba" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hchoi256/mba", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hchoi256/mba with Docker Model Runner:
docker model run hf.co/hchoi256/mba
metadata
license: mit
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen2-VL-7B-Instruct
datasets:
- hchoi256/MBA-Bench
tags:
- multimodal
- benchmark
- vision-language
- agentic-ai
- business-ideation
- qwen2-vl
MBA: Multimodal Benchmark and Agents for Real-World Business Ideation
Official models for MBA: Multimodal Benchmark and Agents for Real-World Business Ideation.
- Paper: https://arxiv.org/abs/2608.11616
- Project Page: https://hchoi256.github.io/projects/mba/
- Code: https://github.com/hchoi256/mba
- Dataset: https://huggingface.co/datasets/hchoi256/MBA-Bench